Next-generation gravitational-wave detectors, with their improved sensitivity and wider frequency bandwidth, will be capable of observing almost every compact binary coalescence signal from epochs before the first stars began to form, increasing the number of detectable binaries to hundreds of thousands annually. This will enable us to observe compact objects through cosmic time, probe extreme matter phenomena, do precision cosmology, study gravity in strong field dynamical regimes and potentially allow observation of fundamental physics beyond the standard model. However, the richer data sets produced by these detectors will pose new computational, physical and astrophysical challenges, necessitating the development of novel algorithms and data analysis strategies. To aid in these efforts, this paper introduces gwforge, a user-friendly, lightweight Python package, to generate mock data for next-generation detectors. gwforge allows users to seamlessly simulate data while abstracting away technical complexities, enabling more efficient testing and development of analysis pipelines. Additionally, the package’s data generation process is optimized using high-throughput systems like HTCondor, significantly speeding up the simulation of large populations of gravitational-wave events. We demonstrate the package’s capabilities through data simulation examples and highlight a few potential applications: performance loss due to foreground noise, bright-siren cosmology and impact of waveform systematics on binary parameter estimation.

中文翻译：

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gwforge：用于生成引力波模拟数据的用户友好型


下一代引力波探测器具有更高的灵敏度和更宽的频率带宽，将能够在第一颗恒星开始形成之前观测到几乎所有来自纪元的致密双星合并信号，从而将可探测的双星数量增加到每年数十万个。这将使我们能够通过宇宙时间观察致密物体，探测极端物质现象，进行精确的宇宙学研究，在强场动力学状态下研究引力，并有可能允许观察标准模型之外的基本物理学。然而，这些探测器产生的更丰富的数据集将带来新的计算、物理和天体物理挑战，需要开发新的算法和数据分析策略。为了帮助这些工作，本文介绍了 gwforge，这是一个用户友好的轻量级 Python 包，用于为下一代检测器生成模拟数据。GWFog 允许用户无缝模拟数据，同时抽象出技术复杂性，从而实现更高效的分析管道测试和开发。此外，该软件包的数据生成过程使用 HTCondor 等高吞吐量系统进行了优化，从而显著加快了大量引力波事件的仿真速度。我们通过数据仿真示例演示了该软件包的功能，并重点介绍了一些潜在的应用：前景噪声导致的性能损失、明亮的警笛宇宙学以及波形系统学对二进制参数估计的影响。